Academic literature on the topic 'Randomised search algorithms'
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Journal articles on the topic "Randomised search algorithms"
Jansen, Thomas, and Christine Zarges. "Analysis of Randomised Search Heuristics for Dynamic Optimisation." Evolutionary Computation 23, no. 4 (December 2015): 513–41. http://dx.doi.org/10.1162/evco_a_00164.
Full textXiao, Peng, Soumitra Pal, and Sanguthevar Rajasekaran. "Randomised sequential and parallel algorithms for efficient quorum planted motif search." International Journal of Data Mining and Bioinformatics 18, no. 2 (2017): 105. http://dx.doi.org/10.1504/ijdmb.2017.086457.
Full textXiao, Peng, Soumitra Pal, and Sanguthevar Rajasekaran. "Randomised sequential and parallel algorithms for efficient quorum planted motif search." International Journal of Data Mining and Bioinformatics 18, no. 2 (2017): 105. http://dx.doi.org/10.1504/ijdmb.2017.10007475.
Full textLissovoi, Andrei, Pietro S. Oliveto, and John Alasdair Warwicker. "On the Time Complexity of Algorithm Selection Hyper-Heuristics for Multimodal Optimisation." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 2322–29. http://dx.doi.org/10.1609/aaai.v33i01.33012322.
Full textLissovoi, Andrei, Pietro S. Oliveto, and John Alasdair Warwicker. "Simple Hyper-Heuristics Control the Neighbourhood Size of Randomised Local Search Optimally for LeadingOnes." Evolutionary Computation 28, no. 3 (September 2020): 437–61. http://dx.doi.org/10.1162/evco_a_00258.
Full textCorus, Dogan, and Pietro S. Oliveto. "On the Benefits of Populations for the Exploitation Speed of Standard Steady-State Genetic Algorithms." Algorithmica 82, no. 12 (July 15, 2020): 3676–706. http://dx.doi.org/10.1007/s00453-020-00743-1.
Full textSerraino, Giuseppe Filiberto, and Gavin J. Murphy. "Effects of cerebral near-infrared spectroscopy on the outcome of patients undergoing cardiac surgery: a systematic review of randomised trials." BMJ Open 7, no. 9 (September 2017): e016613. http://dx.doi.org/10.1136/bmjopen-2017-016613.
Full textHassan, N., R. Slight, D. Weiand, A. Vellinga, G. Morgan, F. Aboushareb, and S. P. Slight. "Predicting infection and sepsis; what predictors have been used to train machine learning algorithms? A systematic review." International Journal of Pharmacy Practice 29, Supplement_1 (March 26, 2021): i18. http://dx.doi.org/10.1093/ijpp/riab016.022.
Full textOliva, Antonio, Gerardo Altamura, Mario Cesare Nurchis, Massimo Zedda, Giorgio Sessa, Francesca Cazzato, Giovanni Aulino, et al. "Assessing the potentiality of algorithms and artificial intelligence adoption to disrupt patient primary care with a safer and faster medication management: a systematic review protocol." BMJ Open 12, no. 5 (May 2022): e057399. http://dx.doi.org/10.1136/bmjopen-2021-057399.
Full textTett, Simon F. B., Kuniko Yamazaki, Michael J. Mineter, Coralia Cartis, and Nathan Eizenberg. "Calibrating climate models using inverse methods: case studies with HadAM3, HadAM3P and HadCM3." Geoscientific Model Development 10, no. 9 (September 28, 2017): 3567–89. http://dx.doi.org/10.5194/gmd-10-3567-2017.
Full textDissertations / Theses on the topic "Randomised search algorithms"
Gutierrez, Soto Claudio. "Exploring the reuse of past search results in information retrieval." Thesis, Toulouse 3, 2016. http://www.theses.fr/2016TOU30034/document.
Full textPast searches provide a useful source of information for new users (new queries). Due to the lack of ad-hoc IR collections, to this date there is a weak interest of the IR community on the use of past search results. Indeed, most of the existing IR collections are composed of independent queries. These collections are not appropriate to evaluate approaches rooted in past queries because they do not gather similar queries due to the lack of relevance judgments. Therefore, there is no easy way to evaluate the convenience of these approaches. In addition, elaborating such collections is difficult due to the cost and time needed. Thus a feasible alternative is to simulate such collections. Besides, relevant documents from similar past queries could be used to answer the new query. This principle could benefit from clustering of past searches according to their similarities. Thus, in this thesis a framework to simulate ad-hoc approaches based on past search results is implemented and evaluated. Four randomized algorithms to improve precision are proposed and evaluated, finally a new measure in the clustering context is proposed
Borenstein, Yossi. "Problem hardness for randomized search heuristics with comparison-based selection : a focus on evolutionary algorithms." Thesis, University of Essex, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.446546.
Full textKarlsson, Albin. "Evaluation of the Complexity of Procedurally Generated Maze Algorithms." Thesis, Blekinge Tekniska Högskola, Institutionen för kreativa teknologier, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-16839.
Full textBjörklund, Henrik. "Combinatorial Optimization for Infinite Games on Graphs." Doctoral thesis, Uppsala University, Department of Information Technology, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-4751.
Full textGames on graphs have become an indispensable tool in modern computer science. They provide powerful and expressive models for numerous phenomena and are extensively used in computer- aided verification, automata theory, logic, complexity theory, computational biology, etc.
The infinite games on finite graphs we study in this thesis have their primary applications in verification, but are also of fundamental importance from the complexity-theoretic point of view. They include parity, mean payoff, and simple stochastic games.
We focus on solving graph games by using iterative strategy improvement and methods from linear programming and combinatorial optimization. To this end we consider old strategy evaluation functions, construct new ones, and show how all of them, due to their structural similarities, fit into a unifying combinatorial framework. This allows us to employ randomized optimization methods from combinatorial linear programming to solve the games in expected subexponential time.
We introduce and study the concept of a controlled optimization problem, capturing the essential features of many graph games, and provide sufficent conditions for solvability of such problems in expected subexponential time.
The discrete strategy evaluation function for mean payoff games we derive from the new controlled longest-shortest path problem, leads to improvement algorithms that are considerably more efficient than the previously known ones, and also improves the efficiency of algorithms for parity games.
We also define the controlled linear programming problem, and show how the games are translated into this setting. Subclasses of the problem, more general than the games considered, are shown to belong to NP intersection coNP, or even to be solvable by subexponential algorithms.
Finally, we take the first steps in investigating the fixed-parameter complexity of parity, Rabin, Streett, and Muller games.
Pourhassan, Mojgan. "Parameterised complexity analysis of evolutionary algorithms for combinatorial optimization problems." Thesis, 2017. http://hdl.handle.net/2440/109799.
Full textThesis (Ph.D.) -- University of Adelaide, School of Computer Science, 2017.
Chisholm, Michael. "Learning classification rules by randomized iterative local search /." 1999. http://hdl.handle.net/1957/11732.
Full textBooks on the topic "Randomised search algorithms"
Theory Of Randomized Search Heuristics Foundations And Recent Developments. World Scientific Publishing Company, 2011.
Find full textBook chapters on the topic "Randomised search algorithms"
Millard, Alan G., David R. White, and John A. Clark. "Searching for Pareto-optimal Randomised Algorithms." In Search Based Software Engineering, 183–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33119-0_14.
Full textGarg, Pramika, Avish Jha, and Amogh Shukla. "Randomised Analysis of Backtracking-based Search Algorithms in Elucidating Sudoku Puzzles Using a Dual Serial/Parallel Approach." In Inventive Computation and Information Technologies, 281–95. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6723-7_21.
Full textDuch, Amalia, Vladimir Estivill-Castro, and Conrado Martínez. "Randomized K-Dimensional Binary Search Trees." In Algorithms and Computation, 198–209. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/3-540-49381-6_22.
Full textTaillard, Éric D. "Randomized Methods." In Design of Heuristic Algorithms for Hard Optimization, 155–70. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13714-3_7.
Full textFister, Iztok, Xin-She Yang, Janez Brest, and Iztok Fister. "On the Randomized Firefly Algorithm." In Cuckoo Search and Firefly Algorithm, 27–48. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02141-6_2.
Full textJansen, Thomas. "Evolutionary Algorithms and Other Randomized Search Heuristics." In Analyzing Evolutionary Algorithms, 7–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-17339-4_2.
Full textClementi, A., L. Kučera, and J. D. P. Rolim. "A Randomized Parallel Search Strategy." In Parallel Algorithms for Irregular Problems: State of the Art, 213–27. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4757-6130-6_11.
Full textLehre, Per Kristian, and Carsten Witt. "Concentrated Hitting Times of Randomized Search Heuristics with Variable Drift." In Algorithms and Computation, 686–97. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13075-0_54.
Full textHui, Lucas Chi Kwong, and Charles U. Martel. "Randomized competitive algorithms for successful and unsuccessful search on self-adjusting linear lists." In Algorithms and Computation, 426–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/3-540-57568-5_274.
Full textWatanabe, Osamu, Takeshi Sawai, and Hayato Takahashi. "Analysis of a Randomized Local Search Algorithm for LDPCC Decoding Problem." In Stochastic Algorithms: Foundations and Applications, 50–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39816-5_5.
Full textConference papers on the topic "Randomised search algorithms"
Liu, Mingmou, Xiaoyin Pan, and Yitong Yin. "Randomized Approximate Nearest Neighbor Search with Limited Adaptivity." In SPAA '16: 28th ACM Symposium on Parallelism in Algorithms and Architectures. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2935764.2935776.
Full textEl-Shaer, Ahmed H., and Masayoshi Tomizuka. "Multi-Objective H2/H∞ Static Output Feedback Control: A Hybrid LMI/Genetic Algorithm Approach." In ASME 2010 Dynamic Systems and Control Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/dscc2010-4126.
Full textMerlin, Jerlin C., and Hieu Dinh. "Poster: Randomized algorithms for planted Motif Search." In 2012 IEEE 2nd International Conference on Computational Advances in Bio and Medical Sciences (ICCABS). IEEE, 2012. http://dx.doi.org/10.1109/iccabs.2012.6182654.
Full textLima, Rafael Zuolo Coppini. "Dimension Reduction for Projective Clustering." In Encontro de Teoria da Computação. Sociedade Brasileira de Computação - SBC, 2022. http://dx.doi.org/10.5753/etc.2022.223040.
Full textVieira, Luciano T., Beatriz de S. L. P. de Lima, Alexandre G. Evsukoff, and Breno P. Jacob. "Application of Genetic Algorithms to the Synthesis of Riser Configurations." In ASME 2003 22nd International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2003. http://dx.doi.org/10.1115/omae2003-37231.
Full textFranti, Pasi, Marko Tuononen, and Olli Virmajoki. "Deterministic and randomized local search algorithms for clustering." In 2008 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2008. http://dx.doi.org/10.1109/icme.2008.4607565.
Full textKumar, Ashok, Suresh Chandrasekaran, A. Chockalingam, and B. Sundar Rajan. "Near-Optimal Large-MIMO Detection Using Randomized MCMC and Randomized Search Algorithms." In ICC 2011 - 2011 IEEE International Conference on Communications. IEEE, 2011. http://dx.doi.org/10.1109/icc.2011.5963229.
Full textJúnior, Darci José Mendes, Luciana Brugiolo Gonçalves, and Stênio Sã R. F. Soares. "An ILS algorithm with RVND for the green vehicle routing problems with time-varying speeds." In XV Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2018. http://dx.doi.org/10.5753/eniac.2018.4452.
Full textCohen, Eldan, Richard Valenzano, and Sheila McIlraith. "Type-WA*: Using Exploration in Bounded Suboptimal Planning." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/557.
Full textNath, Ravindra, and Renu Jain. "Using Randomized Search Algorithms to Estimate HMM Learning Parameters." In 2009 IEEE International Advance Computing Conference (IACC 2009). IEEE, 2009. http://dx.doi.org/10.1109/iadcc.2009.4808999.
Full textReports on the topic "Randomised search algorithms"
Rankin, Nicole, Deborah McGregor, Candice Donnelly, Bethany Van Dort, Richard De Abreu Lourenco, Anne Cust, and Emily Stone. Lung cancer screening using low-dose computed tomography for high risk populations: Investigating effectiveness and screening program implementation considerations: An Evidence Check rapid review brokered by the Sax Institute (www.saxinstitute.org.au) for the Cancer Institute NSW. The Sax Institute, October 2019. http://dx.doi.org/10.57022/clzt5093.
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